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Comparison of different solvent suppression techniques for polymer characterization with a 90 MHz benchtop spectrometer
No full textIn NMR spectroscopy, samples are usually dissolved in deuterated solvents to avoid overlap of small analyte signals with large, protonated solvent signals. However, for reasons such as cost and widespread use, using deuterated solvents is impractical, e.g., for on-flow NMR applications, since large volumes of solvent are required. This study compares six different solvent suppression techniques: PRESATuration (PRESAT), Water suppression Enhanced through T1 effects (WET), Pulsed Gradient STimulated Echo (PGSTE), 1-pulse-spoil, simple solvent subtraction, and a newly developed post-acquisition suppression method named Solvent Attenuation by Fourier Elimination (SAFE). The SAFE method is based on alternating measurements of the sample solution and the pure solvent 2n times, followed by a fast Fourier transform to eliminate the solvent signals, which are constant in the first approximation. The different solvent suppression methods were compared alone and in several combinations to determine their optimum suppression efficiency. The suppression was quantified by evaluating the Analyte-to-Solvent Ratio normalized to the unsuppressed 1H reference spectrum (ASR). Furthermore, a comparison was made between the methods concerning their suitability for polymer solutions of varying molar masses, quantification towards measurement time efficiency, repeatability, and intermediate precision. The PGSTE-SAFE combination proved to be the most efficient method for polymer samples, achieving an ASR of about 47,000. The applicability of solvent suppression methods in flow-based setups was also assessed by investigating polystyrenes in non-deuterated solvents. WET, PGSTE, and a WET-PGSTE combination were applied in online Size Exclusion Chromatography-NMR (SECsingle bondNMR) to demonstrate their potential for efficient solvent suppression in this context
Impact of residuals on recovered nickel-rich LiNiMnxCoO cathodes for direct recycling and reuse
No full textAt present, industrial-scale recycling of lithium-ion batteries typically involves rather energy-intensive processes and toxic solvents to recover, in particular, the metallic elements from the positive electrode active material. These recovered metals subsequently serve as precursors for the synthesis of new electrode materials. One approach to reduce the energy and cost needed is the direct recycling of the electrode active materials. Herein, two recovery methods, namely thermal and solvent-based recovery, are investigated for single-crystalline Ni-rich LiNiMnCoyO (NMC) high-energy cathodes. The NMC obtained via the thermal recovery method exhibits poor performance due to the generation of HF and the degradation of the material. In contrast, the NMC obtained via the solvent-based method, utilizing dimethyl sulfoxide as a non-toxic solvent, demonstrates superior performance, with a reduction in capacity of only 1.5 % compared to pristine NMC. This comparative analysis highlights the critical role of the separation procedure and, particularly, the detrimental effect of any remaining fluorinated binder
Windows of opportunity in subseasonal weather regime forecasting: A statistical–dynamical approach
No full textThe Madden–Julian Oscillation (MJO) and stratospheric polar vortex (SPV) are prominent sources of subseasonal predictability in the extratropics. It has been shown that the joint interaction of the MJO and the SPV can modulate the preferred phase of the North Atlantic Oscillation (NAO) and the occurrence of weather regimes. However, improving numerical weather prediction (NWP) at 3-week lead times remain underexplored. This study investigates how MJO and SPV phases affect Greenland Blocking (GL) activity and integrates atmospheric state information into a neural network to enhance week 3 weather regime activity forecasts. We define a weather regime activity metric using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and reforecasts. In reanalyses we find increased GL activity following MJO phases 7, 8, and 1, as well as weak SPV phases, indicating climatological windows of opportunity in line with previous studies. However, ECMWF forecast skill improves only in MJO phases 8 and 1 and weak SPV phases, identifying somewhat different model windows of opportunity. Next, we explore using these findings in postprocessing tools. Climatological forecasts based on MJO/SPV–NAO relationships provide a purely statistical approach to subseasonal GL activity forecasting, independent of NWP models. Notably, MJO-conditioned climatological forecasts show clear signals when evaluated against observed GL activity. Statistical–dynamical models, using neural networks that combine historical atmospheric state data with NWP-derived weather regime metrics, improve weather regime activity forecasts across all regimes considered, achieving an absolute accuracy increase of 5.8 percentage points in forecasting the dominant weather regime compared with ECMWF. This is particularly beneficial to blocking in the European domain, where NWP models often underperform. Atmospheric conditioned and neural network forecasts serve as valuable decision-support tools alongside NWP models, enhancing the reliability of subseasonal predictions
Meeting report: “An Amazon for workers? Visioneering alternatives for digitalized logistics work”. Workshop, 2025, Leipzig, DE
No full textDirect Search Experiment for Light Dark Matter (DELight): Motivation, status and future perspectives
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Detaillierte Analyse des Auftreffverhaltens eines Kraftstoffstrahls auf eine ölbenetzte Wand unter motorrelevanten Bedingungen
No full textData Requirements for Tire-Road Monitoring: A Roadmap for Data Collection, Processing, and Decision Making
No full textDigitalization of decommissioning activities with integrated autonomous robotics, radiation measurements and 3D visualisation
No full textИскусственный интеллект: ответственные инновации перед лицом потенциальных постепенных дисрупций (Artificial Intelligence: Responsible Innovation in the Face of Potential Gradual Disruptions)
No full textThis paper deals with the possibility of gradual disruptions at the societal level in the course of rapidly advancing digitalization and spread of AI. The term “disruption” refers to the sudden breakdown of familiar, previously stable constellations. Expectations of stability, assumptions of continuity, and planning security are shattered, casting the prospects for the future in an uncertain light. The Latin roots of the term mean “bursting,” “breaking,” and “tearing,” semantically referring to the temporal structure of more or less sudden, abrupt events. Seen in this light, the talk of gradual disruption in the title of this article seems conceptually contradictory or paradoxical. However, there are many examples of disruption in the world of technology that were heralded by recognizable but often unnoticed signs, particularly by material fatigue and wear. The daily stress on many technical objects, such as V-belts in older vehicles or bridge structures, gradually leads to wear and degradation. In this sense, the notion of grad- ual disruption refers to upheavals with significant or even dramatic damage potential that do not occur unexpectedly and suddenly, like a global pandemic or an earthquake, but build up gradually until they finally lead to the disruption of previously stable constellations. I will argue that this type of potential and gradual disruption could emerge in areas of digitalization and AI. Examples include the increasing but unnoticed standardization of human actions, the silent loss of freedom and individuality, the increasing dependence on the smooth functioning of digital infrastructures, the loss of the future as an open space, or the loss of reflection and learning opportunities due to unlimited acceleration. The possibility of such gradual disruptions poses several challenges to responsible research and innovation (RRI), technology assessment (TA), and ethics. These include epistemological issues (how to detect gradual disruptions at an early stage), ethical issues (how to assess and evaluate concerns relating to the precautionary principle, for example), issues of whether countermeasures should be taken, and issues of communication between irrational exaggeration and irrational trivialization. The final part of the paper will address possible gradual disruptions that can be attributed to both technical parameters and human behavior, and draw conclusions for TA and RRI